
'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Tuple   =  ['image_processor', 'tokenizer']
	snake_case__   :List[Any]   =  'ChineseCLIPImageProcessor'
	snake_case__   :Optional[int]   =  ('BertTokenizer', 'BertTokenizerFast')





	def __init__(       self      :							Dict   ,				__magic_name__      :							List[str]=None   ,				__magic_name__      :							List[Any]=None   ,				**__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				None
				if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."   ,				__magic_name__   ,				)
							lowerCAmelCase__										=				kwargs.pop("feature_extractor"				)

				lowerCAmelCase__										=				image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."				)
				if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."				)

				super().__init__(__magic_name__   ,				__magic_name__				)
				lowerCAmelCase__										=				self.image_processor





	def __call__(       self      :							List[Any]   ,				__magic_name__      :							Tuple=None   ,				__magic_name__      :							Any=None   ,				__magic_name__      :							str=None   ,				**__magic_name__      :							List[str]				):


				"""simple docstring"""

				if text is None and images is None:
							raise ValueError("You have to specify either text or images. Both cannot be none."				)

				if text is not None:
							lowerCAmelCase__										=				self.tokenizer(__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__				)

				if images is not None:
							lowerCAmelCase__										=				self.image_processor(__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__				)

				if text is not None and images is not None:
							lowerCAmelCase__										=				image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**__magic_name__				)   ,				tensor_type=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				*__magic_name__      :							Union[str, Any]   ,				**__magic_name__      :							List[str]				):


				"""simple docstring"""

				return self.tokenizer.batch_decode(*__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				*__magic_name__      :							str   ,				**__magic_name__      :							Union[str, Any]				):


				"""simple docstring"""

				return self.tokenizer.decode(*__magic_name__   ,				**__magic_name__				)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.tokenizer.model_input_names
				lowerCAmelCase__										=				self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   ,				__magic_name__   ,				)
				return self.image_processor_class






import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )




@require_torch
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    3
   _UpperCamelCase           =    250

   _UpperCamelCase           =    ids_tensor((batch_size, length)							, lowercase_)
   _UpperCamelCase           =    torch.ones((batch_size, length)							, device=lowercase_							, dtype=torch.float) / length
   return input_ids, scores







 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    StoppingCriteriaList(
       [
           MaxLengthCriteria(max_length=10),
           MaxTimeCriteria(max_time=0.1),
       ])

   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxLengthCriteria(max_length=10)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxNewTokensCriteria(start_length=5							, max_new_tokens=5)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    StoppingCriteriaList([criteria])
   self.assertEqual(criteria_list.max_length							, 10)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1							, initial_timestamp=time.time() - 0.2)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 10)

   with self.assertWarns(lowercase_):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 11)

   _UpperCamelCase           =    validate_stopping_criteria(StoppingCriteriaList()							, 11)

   self.assertEqual(len(lowercase_)							, 1)




